{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\1. 对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。\n",
    "\n",
    "答：对于连续型特征一般采用seaborn.displot函数进行可视化分布，用plt.plot，hist函数也可以，seaborn.displotplt可以同时观察拟合曲线的效果。distplot绘图的纵轴为样本的比例，hist方法绘图的纵轴为样本数目。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 21.,  55.,  82., 154.,  84.,  41.,  30.,   8.,  10.,  21.]),\n",
       " array([ 5. ,  9.5, 14. , 18.5, 23. , 27.5, 32. , 36.5, 41. , 45.5, 50. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（房屋价格）的直方图／分布\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv(\"boston_housing.csv\")  # 导入数据集\n",
    "\n",
    "df[('MEDV')].hist()\n",
    "# df[('MEDV')].plot()\n",
    "# df[features].hist()\n",
    "#sns.distplot(df['MEDV'], bins=30, kde=True) \n",
    "plt.hist(df[('MEDV')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。\n",
    "\n",
    "答：使用corr()函数可以得到相关性，使用heatmap可以得到热力图，观察相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
      "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b5adf77940>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv(\"boston_housing.csv\")  # 导入数据集\n",
    "\n",
    "cols = df.columns #列\n",
    "print(cols)\n",
    "data_corr = df.corr()#相关性\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。\n",
    "\n",
    "答：如果发现特征之间有较强的相关性，可以考虑在特征层面上进行PCA降维处理，在模型层面可以加正则项。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 ，并重新训练最小二乘线性回归、岭回归、和Lasso模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "df = pd.read_csv(\"boston_housing.csv\")  # 导入数据集\n",
    "\n",
    "y = df[\"MEDV\"]\n",
    "\n",
    "X = df.drop([\"MEDV\"], axis = 1)\n",
    "\n",
    "feat_names = X.columns\n",
    "\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# 对训练数据，先调用fit方法训练模型，得到模型参数；然后对训练数据和测试数据进行transform\n",
    "X = ss_X.fit_transform(X)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y = ss_y.fit_transform(y.values.reshape(-1,1))\n",
    "#print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练最小二乘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.690295955089836\n",
      "The r2 score of LinearRegression on train is 0.7451448367308489\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "lr = LinearRegression()#最小二乘\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "\n",
    "lr.fit(X_train, y_train)#训练模型\n",
    "\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6929146520257718\n",
      "The r2 score of RidgeCV on train is 0.7450810988477296\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# 1.设置超参数（正则参数）范围\n",
    "# alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "# 2.生成一个RidgeCV实例\n",
    "# ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "ridge = RidgeCV()\n",
    "\n",
    "# 3.分割测试集和训练集\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "\n",
    "#4.训练模型\n",
    "ridge.fit(X_train, y_train)#训练模型\n",
    "\n",
    "#5.预测模型\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "#训练集\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练Lasso回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.6928010684679107\n",
      "The r2 score of LassoCV on train is 0.7447367213991007\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\tf1\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:1088: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "#1. 设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#2. 生成学习器实例\n",
    "#lasso = LassoCV(alphas=alphas)\n",
    "lasso = LassoCV()\n",
    "\n",
    "# 3.分割测试集和训练集\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "\n",
    "#4.训练模型\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "#5.预测模型\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "#训练集\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\5. 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。\n",
    "\n",
    "答：由上题cell单元中的运行结果：\n",
    "\n",
    "**最小二乘线性回归**结果：\n",
    "\n",
    "    The r2 score of LinearRegression on test is 0.690295955089836\n",
    "    The r2 score of LinearRegression on train is 0.7451448367308489  \n",
    "根据最小二乘线性回归的解(XTX)−1XTY，需要对矩阵XTX求逆。当输入特征存在共线性（某些特征可以用其他特征的线性组合表示），矩阵X是接近不满秩，矩阵XTX接近奇异，求逆不稳定，所以结果不会很理想。所以最小二乘线性回归适用于**特征之间不存在共线性**的情况。由于最小二乘与其他两个模型相比未加正则项，只考虑了对训练样本的拟合程度，故在训练集上的结果最好。\n",
    "\n",
    "**岭回归**模型评价结果如下：\n",
    "\n",
    "    The r2 score of RidgeCV on test is 0.6929146520257555\n",
    "    The r2 score of RidgeCV on train is 0.7450810988477301  \n",
    "从结果来看，岭回归虽然在训练集上的效果不如最小二乘线性回归，但在测试集上的效果却要更好一些。岭回归由于加入了L2正则项，通过alpha参数的调节，可以平衡拟合程度和模型复杂度之间的关系，岭回归的解为：(XTX+λI)−1XTY即使在输入特征存在共线性，矩阵X是接近不满秩，矩阵XTX对角线存在等于0或接近于0的元素，但0+λ≠0，(XTX+λI)求逆仍可得到稳定解。因此岭回归适用于输入**特征存在共线性的情况**。\n",
    "\n",
    "**Lasso回归**模型评价结果如下：\n",
    "\n",
    "    The r2 score of LassoCV on test is 0.6928010684679107\n",
    "    The r2 score of LassoCV on train is 0.7447367213991007\n",
    "从结果来看，Lasso回归模型同样在训练集上的效果不如最小二乘线性回归，但在测试集上的效果却要更好一些，但在此特征下不如岭回归。Lasso回归中的L1正则同岭回归的L2正则一样是用来平衡拟合程度和模型复杂度的，但当正则参数取合适值时，L1正则能使有些线性回归系数为0,得到稀疏模型。所以，Lasso回归模型适用于，当**输入特征多，有些特征与目标变量之间相关性很弱时**，L1正则可能只选择强相关的特征，模型解释性好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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